Discovering collocations via data-driven learning in L2 writing

dc.contributor.author Wu, Yi-ju Ariel
dc.date.accessioned 2021-06-25T17:11:09Z
dc.date.available 2021-06-25T17:11:09Z
dc.date.issued 2021-06-01
dc.description.abstract Adopting the approaches of pattern hunting and pattern refining (Kennedy & Miceli, 2001, 2010, 2017), this study investigates how seven freshman English students from Taiwan used the Corpus of Contemporary American English to discover collocation patterns for 30 near-synonymous change-of-state verbs and new ideas about the topic of “change” in the drafting stage of their essay writing. The study used a mixed-methods approach to examine the learning outcomes, learners’ corpus use, and their perceptions of the process. Results were drawn by analyzing writings in three time frames (pre-test, post-test, delayed post-test), video files of corpus consultation, questionnaires, and stimulus recall-session interviews. The results showed that the learners successfully discovered and incorporated collocation patterns in change-of-state verbs and ideas about the topic of change into their essays, although some difficulties emerged. Their performance on change-of-state verbs improved, and this improvement remained three months after the treatment. The study also demonstrated learners’ different perceptions and actualizations of the affordances offered by the corpus. While all learners used the corpus to correct collocation errors, they had diverse attitudes and uses of the corpus to address content ideas or collocation complexities in their writing. The study concludes by discussing the theoretical and pedagogical implications of the results.
dc.identifier.citation Wu, Y-j. A. (2021). Discovering collocations via data-driven learning in L2 writing. Language Learning & Technology, 25(2), 192–214. http://hdl.handle.net/10125/73440
dc.identifier.issn 1094-3501
dc.identifier.uri http://hdl.handle.net/10125/73440
dc.publisher University of Hawaii National Foreign Language Resource Center
dc.publisher Center for Language & Technology
dc.publisher (co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
dc.subject Corpus-assisted Learning
dc.subject Collocation Competence
dc.subject L2 Writing
dc.subject Reference Resources
dc.title Discovering collocations via data-driven learning in L2 writing
dc.type Article
dc.type.dcmi Text
prism.endingpage 214
prism.number 2
prism.publicationname Language Learning & Technology
prism.startingpage 192
prism.volume 25
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
25_02_10125-73440.pdf
Size:
657.87 KB
Format:
Adobe Portable Document Format
Description: